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Making Open Data more Personal Through a Social Value Perspective: a Methodological Approach

Author

Listed:
  • Federico Cabitza

    (Università degli Studi di Milano-Bicocca
    IRCCS Istituto Ortopedico Galeazzi)

  • Angela Locoro

    (Università degli Studi di Milano-Bicocca)

  • Carlo Batini

    (Università degli Studi di Milano-Bicocca)

Abstract

In this paper, we address the problem of assessing the social value of open data. While the number of open data initiatives increases and many data sets are currently available to lay people, common citizens and end users, a still limited number of studies specifically address how to improve open data understandability, their usability by common users and the measurability of their value in terms of concrete outcomes and benefits for the intended communities and the individual who appropriate those data, to make them more personal and hence more valuable. Our goal is to contribute to the success of open data initiatives by defining a methodology by which to assess their perceived social value. In this paper, we present the conceptual content of the methodology, that is its main concepts and logic structure, and discuss it by means of an empirical user study in which we applied it to real-life open data sets and involving a large sample of prospective consumers of those open data. In particular, we focus on the health care domain in order to improve the welfare of the citizens that need health care services and use the Web to look for relevant information to address those situated needs. Among our main findings, we discovered a clear preference for visual information formats by women with respect to men, and a clear preference for hospitals ranking by disease for senior people with respect to younger.

Suggested Citation

  • Federico Cabitza & Angela Locoro & Carlo Batini, 2020. "Making Open Data more Personal Through a Social Value Perspective: a Methodological Approach," Information Systems Frontiers, Springer, vol. 22(1), pages 131-148, February.
    • Handle: RePEc:spr:infosf:v:22:y:2020:i:1:d:10.1007_s10796-018-9854-7
    DOI: 10.1007/s10796-018-9854-7
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    References listed on IDEAS

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    2. A. Geethapriya & S. Valli, 2021. "An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis," Information Systems Frontiers, Springer, vol. 23(3), pages 791-805, June.

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